In online handwritten character recognition, it is effective to narrow down the candidate characters as a form of preprocessing. This is important in order to allow a character type having many categories, such as kanji, to be recognized in real time, particularly in putting a recognition technique into practical use.
Usually, such restriction of candidate characters is first performed using the number of strokes. For instance, candidate characters are narrowed down to the range Number of Strokes K (K is determined according to the number of input strokes or the like). However, with the number of strokes only, it is difficult to obtain a high restriction rate with high precision. To obtain further restriction in addition to restriction by the number of strokes, a complicated procedure has been required for extracting the characteristic amount of a handwritten character, and a great deal of manpower has been needed to prepare a dictionary. For instance, in Sakuraniwa, Y., Yamaguchi, H. and Magome, Y., "Online Handwritten Character Recognition Using Fuzzy Set Theory," Electronic Information Communication Society Articles (D), Vol. J72-D-II, No. 12, pp. 2032-2040, December 1989, the intersection and curved point numbers of strokes are used as characteristic amounts, but extraction of these requires a series of complex procedures such as linear approximation by DP matching, intersection detection by multiplication and division, and stroke order reconstruction. In addition, since the maximum or minimum value of a characteristic amount is used, a slight amount of noise or incorrect characters contained in the handwritten character samples used to extract them can adversely affect the extracted value. In consequence, a great deal of manpower is required to carefully remove noise and incorrect characters. Furthermore, since in principle it is required that the number of strokes must be correctly written, the application range is limited.
There are methods which make the recognition technique itself fast through simplification and use it to narrow down the candidate characters, as shown in the following examples. In Hirose, H., Tasaka, S. Morita, T., Horii, M. and Ida, T., "Online Handwritten Kanji Recognition System," Sharp Technique 1984, No 28pp. 69-74, the candidate characters are narrowed down through simplification by the performance of a pattern matching method that absorbs stroke order variation only at the starting point of a stroke. In addition, in Wakahara, T. and Umeda, M., "Online Classification of Characters Written in Cursive Style Using Stroke Connection Rule," Articles of Institute of Electronics and Communication Engineers of Japan (D), Vol. J67-D, No. 11, pp. 1285-1292, November 1954, the candidate characters are narrowed down through simplification by the performance of a selective stroke connection method that absorbs variation in the number of strokes and stroke order only at the starting and ending points of a stroke. To absorb variation in stroke order, however, both need to match the strokes of the handwritten characters and the dictionary characters with each other for all combinations. This indicates that an amount of operations proportional to the product of both numbers of strokes is necessary, and thus these are methods which basically require a large amount of operations.
As patent references in the field of this invention, there are Published Unexamined Patent Application Nos. 57-132283, 60-89289 and 63-129488. PUPA No. 57-132283 discloses an approach in which distance is calculated for each stroke in online handwritten character recognition and recognition is performed according to the sum of the distances. However, there is no description of folding the characteristic amounts of a plurality of sample strokes into one word. In addition, the PUPA No. 60-89289 employs an approach similar to the PUPA No. 57-132283. However, this also has no description of folding the characteristic amounts of a plurality of sample strokes into one word. PUPA No. 63-129488 discloses that plural types of reference characteristic amounts are stored for each character, and they are interpolated and used as characteristic amounts for comparison. But, this also has no description of folding the characteristic amounts of a plurality of sample strokes into one word and performing candidate restriction by simple operations of AND and OR.